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\begin{document}

\title{Effects of Lake level changes on water quality and fisheries production
of Lake Baringo, Kenya}



\author[1]{Jacques Walumona}%
\author[1]{Boaz Arara}%
\author[2]{Cyprian Ogombe}%
\author[2]{James Murakaru}%
\author[1]{Phillip Raburu}%
\author[3]{Fabrice Amisi}%
\author[2]{Kobingi Nyakeya}%
\author[4]{Benjamin Kondowe}%
\affil[1]{University of Eldoret}%
\affil[2]{Kenya Marine and Fisheries Research Institute}%
\affil[3]{Institut Supérieur Pédagogique de Bukavu}%
\affil[4]{Mzuzu University}%


\vspace{-1em}



  
  \date{June 17, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
The study was conducted in Lake Baringo and determined quantitative
relationships between water level changes, water quality, and fishery
production for informed lake basin management. Long-term (2008 to 2020)
data on water level, water quality, and fisheries yields from Lake
Baringo were analyzed using a combination of statistical methods. Linear
and waveform regression analyses described patterns of lake level
fluctuations over time while, Pearson's correlation determined the
concordance of lake level changes with water quality parameters,
landings, and condition of fish species. PCA results grouped the study
period into different years based on annual water quality variable
levels. LOWESS analysis showed the decline of annual lake level
amplitude over time with peak values in 1964 (8.6 m) and 2008 (9.4 m).
The waveform regression significantly modeled lake level fluctuations as
indexed by annual deviations from the long-term average (DLTM) and
showed a 20-year oscillation between peak water levels in the lake.
There were significant positive correlations of Water Level Fluctuations
(WLFs) with water quality variables and water quality index (WQI) in
Lake Baringo. Linear regression analyses showed a significant
concordance (p \textless{} 0.05) between the annual fishery yield and
the rising WLFs (r = 0.66). Overall, the results demonstrate that WLFs
of Lake Baringo are a driver of fish species biomass and
physico-chemical properties of the lake. We recommend the integration of
fisheries yields, water quality assessment, and WLFs modeling at
different temporal scales in the management of Afrotropical lake
ecosystems%
\end{abstract}%



\sloppy


Effects of Lake level changes on water quality and fisheries production
of Lake Baringo, Kenya

Walumona Riziki Jacques\textsuperscript{1,3*}, Kaunda-Arara
Boaz\textsuperscript{1}, Odoli Ogombe Cyprian\textsuperscript{2}, Mugo
James Murakaru\textsuperscript{2}, Raburu Philip\textsuperscript{1},
Muvundja Amisi Fabrice\textsuperscript{3,5}, Nyakeya
Kobingi\textsuperscript{2}, and Benjamin N. Kondowe\textsuperscript{1,4}

\emph{\textsuperscript{1}University of Eldoret, Department of Fisheries
and Aquatic Sciences, P.O. Box 1125-30100, Eldoret, Kenya.}

\emph{\textsuperscript{2}Kenya Marine and Fisheries Research Institute,
Baringo Station, P.O. Box 231-30406, Marigat, Kenya.}

\selectlanguage{ngerman}\emph{\textsuperscript{3}Unité d'Enseignement et de Recherche en
Hydrobiologie Appliquée (UERHA), Département de Biologie-Chimie,
Institut Supérieur Pédagogique de Bukavu (ISP), B.P. 854 Bukavu, RD
Congo.}

\emph{\textsuperscript{4}Mzuzu University Department of Fisheries and
Aquatic Sciences, P/Bag 20, Luwinga Mzuzu 2, Malawi.}

\textsuperscript{5}Centre des Recherches en Environnement et
Géo-Ressources, Catholic University of Bukavu (UCB), BP. 285 Bukavu, D.
R. Congo.

\textbf{*Corresponding author}

ABSTRACT

The study was conducted in Lake Baringo, a shallow rift valley lake in
Kenya, and determined quantitative relationships between water level
changes, water quality, and fishery production for informed lake basin
management. Long-term (2008 to 2020) data on water level, water quality,
and fisheries yields from Lake Baringo were analyzed using a combination
of statistical methods. Linear and waveform regression analyses were
used to describe patterns of lake level fluctuations over time while,
Pearson's correlation was used to determine the concordance of lake
level changes with water quality parameters, landings, and condition of
fish species. PCA results grouped the study period into the years (2008
to 2012) marked by higher TP, turbidity levels, and Water Quality Index
(WQI) values demonstrating the periods of poor water quality, and
periods (2015 to 2020) of good water quality characterized by decreased
TP, low turbidity and low WQI values attributed to higher annual lake
water levels. Locally-weighted scatter plot smoother (LOWESS) analysis
showed the annual lake level amplitude to have generally declined over
time with peak values in 1964 (8.6 m) and 2008 (9.4 m). The waveform
regression significantly modeled lake level fluctuations as indexed by
annual deviations from the long-term average (DLTM) and showed a 20-year
oscillation between peak water levels in the lake. The lake water
quality increased during the years when DLTM values were \selectlanguage{english}[?] 2 m. There
were significant positive correlations of Water Level Fluctuations
(WLFs) with water quality variables (conductivity, depth, TP,
PO\textsubscript{4}\textsuperscript{3--}, DO, temperature, turbidity,
NO\textsubscript{2}\textsuperscript{--},
NH\textsubscript{4}\textsuperscript{+} and
NO\textsubscript{3}\textsuperscript{--}) and water quality index (WQI)
in Lake Baringo indicating lake water quality deteriorated during lower
water levels and improved during rising water levels. Linear regression
analyses showed a significant concordance (p \textless{} 0.05) between
the annual fishery yield and the rising WLFs (r = 0.66). Also, there was
a significant (p \textless{} 0.001) relationship between the condition
factor of the native species, \emph{oreochromis niloticus baringoensis}
, and the yearly lake level amplitude (r = 0.69) while, catches of the
lungfish, \emph{protopterus aethiopicus} and \emph{Barbus
intermedius,}showed a differing relationship with WLFs in the lake
indicating species-specific influence of WLFs on catches from the lake.
Overall, the results demonstrate that WLFs of Lake Baringo are a driver
of fish species biomass and physico-chemical properties of the lake. We
recommend the integration of fisheries yields, water quality assessment,
and WLFs modeling at different temporal scales in the management of
Afrotropical lake ecosystems.

Keywords: Fisheries, water quality parameters, waveform and Gaussian
models, and watershed management.

INTRODUCTION

Water level fluctuations in aquatic systems affect their
physico-chemical properties, assemblage structures, ecosystem functions,
and ecological services (Coops and Hosper, 2002). Water level changes in
lakes can be caused by natural factors such as climatic variability
(Bergonzini \emph{et al} ., 2004; Gownaris \emph{et al} ., 2015), human
influences such as through damning abstraction (Evtimova and Donohue,
2014) and through climatic forcing leading to extreme variability in
rainfall and drought conditions (Dudgeon et al., 2006). The changes in
the lake water level may intern affect physico-chemical parameters
through volume changes and productivity of the system through
ecohydrological influences (Zalewski \emph{et al} ., 1997; Coops and
Hosper, 2002). The influence of water level variations on water quality
and fisheries production has been widely studied in most temperate
rivers, lakes, and reservoirs (Welcomme 1970; Hamerlynck \emph{et al} .,
2011; Kolding \emph{et al} ., 2016). However, there has been a paucity
of studies in tropical freshwater bodies and especially the Afrotropical
systems. Changes in water level fluctuations (WLFs) tend to affect lake
productivity through nutrient supply variations, influence on breeding
areas, and changes in shallow productive inshore areas (Kolding and van
Zwieten, 2012; Gownaris \emph{et al} ., 2015; Musinguzi \emph{et al} .,
2019) in addition to changes in water quality parameters (White \emph{et
al} ., 2008).

The physico-chemical properties of water bodies play a significant role
in various aspects of their ecohydrobiology (the relationship between
hydrology and ecology) (Zalewski \emph{et al} ., 1997). The interactions
of physico-chemical properties of water have a significant role in the
composition, distribution, and abundance of aquatic organisms
(Hinckley\emph{et al} ., 2014). Lake Baringo is a shallow freshwater
rift valley lake in Kenya with a surface area of about 140
km\textsuperscript{2} and designated as a Ramsar site (Ramsar, 2002).
The lake is facing human-induced and natural stressors as a result of
land and water use systems as well as climate variability, amongst
others (Omondi \emph{et al} ., 2014). Additionally, Lake Baringo is
characterized by low water depths (average of 9.5 m, as in 2020,
Walumona, personal observation), the mixing of surface and bottom water
induced by the wave actions together with the clay soils of the
catchment contributes to the lake's notable high turbidity, reported to
affect the primary productivity of the lake (Hinckley \emph{et al} .,
2014; Odada \emph{et al.} , 2006; Nyakeya \emph{et al.} , 2020). The
lake has a long history of water level fluctuations (Hickley \emph{et
al} ., 2004; Aura \emph{et al} ., 2020). Although there have been
reports of the influence of these fluctuations on its fishery
(Kallqvist, 1987; Omondi \emph{et al} ., 2011), these have been mostly
on an inter-annual or semi-annual basis and have not accounted for
long-term inter-annual variability. Additionally, the lake water level
changes are likely to affect physico-chemical parameter values, however,
despite the periodic fluctuations in the water level of Lake Baringo,
there are no accounts of how these fluctuations have affected its water
quality parameters and ecological functions including, fisheries yields.
In this study, we hypothesized that both intra- and inter-annual changes
in the levels of Lake Baringo will affect the water quality parameters
and fisheries, with cascading influences on lake functions and
livelihoods.

MATERIALS AND METHODS

\emph{Study area}

The study was conducted in Lake Baringo (Figure 1), a shallow (mean
depth = \textsubscript{\textasciitilde{}} 9.5 m, Walumona, personal
observation) freshwater lake in the eastern Rift Valley of Kenya. The
lake lies at 0deg36'N, 36deg04'E, and approximately 60 km north of the
equator at an altitude of 975 m above mean sea level. It is also a
source of freshwater used for domestic purposes by the local population
(especially for drinking), livestock and supports a substantial fishery
in a semi-arid area. Its fishery has been reported to be poor and
originally composed of five species. Three (\emph{Oreochromis niloticus
baringoensis} , \emph{Clarias gariepinus} and \emph{Protopterus
aetiopicus} ) of species are of commercial value while, \emph{Barbus
spp.} now rarely appears in fisher's catches and \emph{Labeo spp.} has
almost disappeared in the lake (Aloo, 2002). The decreased fish
diversity is thought to be due to overfishing pressures but could also
be attributed to limnological changes (Hickley \emph{et al} ., 2004).

The lake surface is reported to cover slightly over 130
km\textsuperscript{2} with wide \selectlanguage{english}fluctuations as a consequence of water
level fluctuations due to climatic influences (Kallqvist, 1987;
Hickley\emph{et al} ., 2004). The catchment area is about 6820
km\textsuperscript{2} and includes a large part of the western
escarpment of the Kenyan Rift Valley where most of the water is derived
from.

The climate of the region is ``semi-arid'' characterized by two rainy
seasons with an annual average of about 600 mm (Omondi \emph{et al} .,
2014). Due to heavy rains experienced in 2011 in the Eastern African
region, the lake water surface increased dramatically to 207
km\textsuperscript{2} in 2016 (Obando \emph{et al} ., 2016) and then to
more than 250 km\textsuperscript{2} in 2020. The dry season usually
starts from September to February with no rain in January while, the
rainy season occurs between March and August (Odada \emph{et al.,}2006).
The precipitation in the lake area ranges from about 600 mm on the east
and south of the lake to 1500 mm on the western escarpment of the Rift
Valley. Lake Baringo faces a very high annual evaporation rate of
1650-2300 mm (Odada \emph{et al} ., 2006). The lake has no known outflow
and is supplied by inflows from seasonal (Endao, Lokesen, Makutani, and
Ol Arabe) and perrenial (Perkerra and Molo) rivers (Figure 1). Lake
Baringo is believed to have an underground seepage that maintains its
freshness by losing approximately 108 m\textsuperscript{3}yr
\textsuperscript{-1} (Dunkley \emph{et al} ., 1993).

\emph{Data collection}

Lake water levels (WLs) data were obtained from the Kenya Water
Resources Authority (WARA) for the period from 1956-2018. The water
level is monitored by a graduated wooden scale of 5 m height with the
nearest 1-cm accuracy. The scale was installed at the lowest elevation
in the southern part of the lake (Figure 1) to enable monitoring even
during low lake levels. Additional WL data were obtained by the project
from January 2020 to April 2021 using a similar scale and the same
location. For each year of record, both for the two periods (1956-2018)
and ( 2020-2021), both monthly and yearly mean lake levels were
calculated and used to derive the lake water level fluctuation (WLF)
indices as described below. Both the water quality parameters and fish
landings were measured from three ecological zones (northern, central,
and southern) in the lake extending from the northern to the southern
parts of the lake (Figure 1) in order to cover the entire lake area. The
characteristics of the three ecological zones of Lake Baringo are
described in Walumona \emph{et al} . (2021). A total of 9 stations (N1,
N2, N3, C1, C2, C3, S1, S2, and S3), three from each zone, were then
selected in the three ecological zones of the lake for water quality
sampling. Station replicates are numbered according to the three zones
and are shown in Figure 1.

\emph{Water Level Fluctuations (WLFs) indices}

Two differently time-lagged indices were used as indicators of water
level fluctuations in Lake Baringo as proposed by White \emph{et
al.}(2008): (1) the difference from the long-term mean (DLTM) (m) was
calculated by determining the mean water level from 1956 to 2020 for the
lake and then subtracting that mean value (across years) from the mean
water level for a particular year. These calculations result in positive
and negative values indicating the mean water level for a particular
year relative to the lake's overall long-term mean. (2) the annual
amplitude (WLamp, m) determined as the difference between the lowest and
highest recorded lake level within a year (WLmax-WLmin) provides a
measure of the strength of the flood pulse for that particular year.

\emph{Water quality parameters}

The following water quality parameters were measured in the lake:
conductivity, turbidity, chlorophyll-a (Chl-a), depth, DO, temperature,
TP, PO\textsubscript{4}\textsuperscript{3--},
NO\textsubscript{3}\textsuperscript{--}, TN,
SiO\textsubscript{4}\textsuperscript{4--},
NH\textsubscript{4}\textsuperscript{+}. The data were collected monthly
from the 9 stations from March 2008 to June 2019 and again from January
2020 to April 2021. Temperature, dissolved oxygen, and conductivity were
measured \emph{in situ} using a multiparametric field probe (YSI
Incor.550, USA); turbidity with a turbidimeter (HACH 21000, Germany).
The water depth at each sampling station was determined by an echo
sounder (Plastimo Echotest II, 59588, France). Water samples for
nutrients (TP, PO\textsubscript{4}\textsuperscript{3--},
NO\textsubscript{3}\textsuperscript{--}, TN,
SiO\textsubscript{4}\textsuperscript{4--}, and
NH\textsubscript{4}\textsuperscript{+}) and Chl-a analyses were taken at
each station using a small 5-liter polyethylene canister, previously
rinsed with distilled water. Before the nutrient analyses, water samples
were filtered using Macherey-Nagel GF/5 filters with a porosity of 0.7
\selectlanguage{greek}μ\selectlanguage{english}m (Bartram and Ballance 1996). Both filtered water and labeled vials
were placed in a portable freezer at 4\selectlanguage{ngerman}°C and later stored in a fridge at
-20 °C for further nutrient and chlorophyll-a analyses in the
laboratory. Phosphate, Ammonium, Nitrites, Silica, and Chl-a were
analyzed on filtered water using various standardized techniques of
UV-Visible spectrophotometric analysis of water samples (APHA, 2005;
Rodier, 2009). The laboratory details of sample analysis procedures are
described in Walumona \emph{et al} . (2021).

In order to evaluate how lake level changes may affect the water quality
of the Lake, a water quality index (WQI) was derived (Bhateria and Jain,
2016). The WQI included the parameters given in Table I and was
calculated as an indicator of water pollution in two steps using the
Bhateria and Jain (2016) method.

1°) determination of a subjective water quality index by using k
coefficient:

\(\text{WQI}_{\text{sub}}=k\frac{\sum_{i=0}^{n}{\text{Ci}\ \text{Pi}}}{\sum_{i=0}^{n}\text{Pi}}\)(1)

Where, \emph{k} is a subjective coefficient with a value ranging between
0.25 (mostly for heavily contaminated water indicated by blackish color,
harsh odor, visible fermentation, etc.) and 1 (apparent contamination,
clear or with natural suspended solids), \emph{n} is the total number of
parameters, \emph{Ci} is the value assigned to parameter \emph{i} after
normalization and \emph{Pi} is the relative weight assigned to each
parameter with a value between 1 and 4, where 4 is assigned to a
parameter that is most critical for the preservation of aquatic life
(e.g. dissolved oxygen) and 1 is assigned to the parameter that has a
less direct impact (e.g. temperature and pH) (Table I). Ci is a
normalization factor, to make the WQI more objective and realistic
(Herna'ndez-Romero \emph{et al} ., 2004).

2°) Following normalization, an objective WQI was calculated using k = 1
in order to account only for variations due to the parameters measured
in situ as:

\(\text{WQI}_{\text{obj}}=\frac{\sum_{i=0}^{n}{\text{Ci}\ \text{Pi}}}{\sum_{i=0}^{n}\text{Pi}}\)(2)

\emph{Fish landings}

Data on fish landings (1982-2018) from the lake were obtained from the
monthly records of the Kenya Fisheries Department at Baringo Station and
by sampling the landings from January 2020 to April 2021. The fish
landings data represented catches from the entire lake and were derived
from representative catches from the northern, central, and southern
parts of the lake.

The data collected included weight (0.1 kg) and length (nearest mm) for
each specimen of species landed randomly sampled from the fishers. The
relative condition factor (Kn) was estimated for each species over the
same period (2008-2020) from the ratio of observed weight to expected
weight for length following Le Cren's equation (Le Cren, 1951).

\emph{Statistical analysis}

Principal components analysis (PCA) was used to group the years
(2008-2020) based on physico-chemical variables and water level
fluctuations. Linear regression (y = ax + c) and waveform sine
regression (y = a*sin (2*p*x/b + c) analyses were performed to determine
the best-fitting model to explain the patterns of lake level
fluctuations (WLFs) over the years. Where both models were not
significant, a Locally Weighted Scatter Plot Smoother (LOWESS,
Cleveland, 1979) was used to describe the pattern of lake level
fluctuations. LOWESS is based on a weighted least squares algorithm that
gives local weights the most influence while minimizing the effects of
outliers. A smoothness parameter (f) of 0.2 was found to adequately
smooth the data without distorting the temporal patterns.

Pearson's correlation coefficient was used to determine the concordance
between WLF indicators (DLTM and Amplitude, WLamp) and fisheries
variables and with water quality parameters (conductivity, turbidity,
chlorophyll-a, depth, DO, temperature, TP,
PO\textsubscript{4}\textsuperscript{3--},
NO\textsubscript{3}\textsuperscript{--}, TN,
SiO\textsubscript{4}\textsuperscript{4--},
NH\textsubscript{4}\textsuperscript{+} and WQI). Both Pearson's and
linear regression analyses were conducted on log (x + 1) transformed
data to meet the required assumption of normality of the dataset (Zar,
2010). The frequency distribution displayed as a histogram of pixel
depth was used to group the sampling period in years from 1956 to 2021
based on the increased or decreased rate of the depth while, the lake
water level -- lake surface area relationship was determined using a
linear regression model.

Linear regression and non-linear Gaussian distribution (Zar, 2010) were
used to determine the influence of WLFs on the lake's fishery yields and
condition factor as a measure of growth. The Gaussian distribution
follows a unimodal pattern and tested the hypothesis that the lake
fisheries production and fish condition will correspond to optimum WLFs
levels below and above which a decline is realized. All the graphical
plots were implemented in the Sigma Plot software package.

RESULTS

\emph{Temporal variation in lake properties}

The results of PCA ordination showed a clear separation between the
years based on the lake water quality properties and fisheries yields
for a 13-year time frame (Figure 2). Of the five axes extracted in the
PCA, only axes 1 and 2 are presented as they explain the majority of the
extracted variance, 93.84\% in total, 81.25 \%, and 12.59 \%,
respectively.

The years 2013 and 2014 ordinated in the upper left of the biplot and
are characterized as having high TN concentrations and total fish yields
while, the years from 2008 to 2012 are ordinated to the lower right and
characterized by having higher turbidity concentrations ranging between
73.69-185 NTU, higher WQI values fluctuating between 860.19 and 1443.47,
higher TP levels varying from 146.66 to 240.35 \selectlanguage{greek}µ\selectlanguage{english}g.
L\textsuperscript{-1}indicating periods of poor water quality and high
yearly water level fluctuations measured as yearly amplitude (1.56-9.36
m). Besides, the years 2015 to 2020 that were ordinated in the upper and
lower left of the biplot are characterized by having higher DLTM,
indicating an increase in the annual lake water levels relative to the
long-term average. The other water quality parameters such as DO and
temperature, known to affect assemblages and lake function, were not
strong descriptors in the grouping of years using the first two
components (Figure 2).

\emph{Water Level Fluctuation Patterns}

The lake water levels fluctuated by yearly amplitude (WLamp \selectlanguage{ngerman}± SD) of
2.25 ± 2.00 m from 1956 to 2020. Consequently, a LOWESS plot showed that
the lake water level annual amplitude fluctuated during the period 1956
to 1975 and then decreased steadily up to its lowest level in 1989 (
amplitude = 0.1 m), with a subsequent increase to peak amplitude in 2008
(9.4, m) before a decreasing fluctuation between 2008 and 2021 (Figure
3). Linear regression analysis showed a non-significant (\emph{p} =
0.12) negative relationship between yearly amplitude and time while the
waveform Sine 3 parameter modeled as Wlamp =2.328*sin
(2*\selectlanguage{english}[?]*year/43.45+6.28) unlikely revealed a significant result () even
though \emph{p} \textless{} 0.05 but r2 = -0.64 indicating a poor fit
than a horizontal line. The trends in water level fluctuations as
measured by DLTM (Figure 4) were poorly explained by linear regression
model (\emph{p} = 0.1229, r\textsuperscript{2} = 0.059). However, the
waveform Sine (3 parameter) model: DLTM = 1.376*sin (2*\selectlanguage{english}[?]*year/(42.98 +
6.28)) was highly significant although with a weak fit (P \textless{}
0.0001, r2 = 0.21), and indicated peak rise in water levels after every
\selectlanguage{english}~20 years (Figure 4).

The depth frequency plot (Figure 5a) showed that for 25\% of the years
(1956-2021), the average depth of the lake was about 2 m while, for 20\%
of the years, the average depth was 3 m. For less than 10\% of the
years, the mean depth of the lake reached 4 m. Additionally only for
less than 5\% of the years, the lake means depth ranged between 10-14 m
during the period from 1956-2021. The lake depth-area relationship
showed significant dependence (R\textsuperscript{2} = 0.74) of the lake
depth on its area (A, km\textsuperscript{2}) modeled as: depth =
0.179e\textsuperscript{0.019A}; Figure 5b. Thus, in current conditions,
a 1m increase in lake depth leads to a
\textsubscript{\textasciitilde{}}90 km\textsuperscript{2} increase in
lake area with likely influence on the riparian communities through
overflows.

\emph{Relationship between lake level fluctuations (WLFs) and water
quality parameters}

A significant correlation (\emph{p} \textless{} 0.01) was found between
nine water quality parameters (conductivity, depth, TP,
PO\textsubscript{4}\textsuperscript{3--}, WQI, DO, temperature,
turbidity, and NO\textsubscript{3}\textsuperscript{--}) and DLTM (Table
II). Only, one parameter (SiO\textsubscript{4}\textsuperscript{4--}) was
significantly (\emph{p} \textless{} 0.01) correlated with the yearly
amplitude (WLamp) in Lake Baringo indicating the DLTM to be a better
predictor of water quality in the lake at an inter-annual scale. Except
for the depth and different forms of nitrogen
(NO\textsubscript{3}\textsuperscript{--},
NO\textsubscript{2}\textsuperscript{--} and
NH\textsubscript{4}\textsuperscript{+}) which had negative correlations
with DLTM, other parameters demonstrated positive concordance with
increasing DLTM over time in the lake.
NH\textsubscript{4}\textsuperscript{+} that ranged from 21.75 to 48.8 \selectlanguage{greek}µ\selectlanguage{english}g
L\textsuperscript{-1}, also showed significant negative correlation (r =
-0.64, \emph{p} \textless{} 0.05) with DLTM over time in the lake.

Silicate ions (SiO\textsubscript{4}\textsuperscript{4--}) ranged from
18.92 to 27.43 mg L\textsuperscript{-1} across all years (2008-2021).
SiO\textsubscript{4}\textsuperscript{4--} did not demonstrate a
concordance with DLTM but showed a positive significant correlation with
yearly amplitude of lake levels (r = 0.70, p = 0.008) indicating the ion
is tracked by short-term changes in the lake. There was no significant
concordance between Chl-a, and TN with any of the WLFs indices over time
in the lake. The water quality index (WQI) as a measure of the lake
water quality was significantly correlated with the DLTM (r =
0.80,\emph{p} = 0.001) and not the lake water level amplitude, WLamp
(Table II). The Gaussian 4 parameter unimodal model further provided a
significant fit (\emph{p} \textless{} 0.05) to the relationship between
WQI and DLTM (R\selectlanguage{ngerman}² = 0.89) and not with the lake amplitude (Figure 6a and
b) indicating that a range of DLTM of \selectlanguage{english}[?] 2 m and in the range of 6.5 to
\selectlanguage{english}[?]10 m provided a good water quality of the lake as WQI approached the
WHO recommended value of WQI \selectlanguage{english}[?] 100, while DTLM values of \selectlanguage{english}[?] 2 m resulted
into poor water quality. The Gaussian, 4-parameter model: WQI = 314.68 +
1252.05*exp(-0.5*((x - 3.88)/2.2)2) yielded a significant relationship
(\emph{p} = 0.0024 ) between WQI and DLTM (R2 = 0.89) (Figure 6). These
results indicated that the water quality index (WQI) was lowest in years
closest to the highest long-term mean (DLTM = 9.9 m) and increased with
decreasing lake water levels.

\emph{Relationship between lake level fluctuations and fisheries}

The reported yield of Lake Baringo's fisheries has fluctuated greatly
since the early 1980's. Annual yields from 1982-2020 (years of available
data on annual fisheries) ranged from approximately 8 metric tons in
1994 to 496 metric tons in 2017 averaging close to 227 metric tons per
year (Figure 7). Linear regression analysis showed a significant
relationship (p \textless{} 0.05) between annual fisheries yields and
the WLs (amplitude) indicating the direct effect of WLFs on the lake's
fisheries production (Fishery yield = 78.15 + (29.94 * WLS); R2 = 0.66;
Figure 7). These results suggest that water level changes have an
influence on fish catchability in the lake.

The fishery variables (fish yields and fish condition factor) were
correlated with the WLFs indicators as per the Pearson's correlation
coefficients (r) shown in Table III. The results indicated a strong
significant positive relationship (\emph{p} \textless{} 0.001) between
the condition factor of the endemic tilapia species, \emph{Oreochromis
niloticus baringoensis} , with the yearly lake water level amplitude (r
= 0.69). However, there were potential significant correlations (r = \selectlanguage{english}~
0.5) between the condition factor of the lungfish, \emph{Protopterus
aethiopicus} with DLTM (r = 0.48), and between the annual yield of the
barb, \emph{Barbus intermedius,} and DLTM (r = 0.50) (Table III). These
results demonstrated positive and negative relationships for \emph{P.
aethiopicus} and \emph{B. intermedius} with mean
WLFs\emph{,}respectively. There were no significant relationships
between either DLTM or amplitude (WLFs) and total biomass as well as
with fish species yields in the lake except for \emph{B. intermedius}
(Table III).

DISCUSSION

The results demonstrated inter-annual water level changes in Lake
Baringo and the existence of patterns across years for the long-term
database (1956-2020). They also indicated a near two decades periodicity
or oscillation in extreme peak water levels across a 64-year time frame.
This oscillation is likely associated with periodicity in abnormal
rainfall events due to climate cycle variability in the lake watershed
over time (Ngaira 2006; Aur \emph{et al.,} 2020). The mean annual WLs
and amplitude intensities have been fluctuating in the Lake Baringo
watershed over the last six decades as also evidenced in other African
lakes (Kolding and Van Zwieten, 2012) and elsewhere (Blenckner, 2005;
Neckles, \emph{et al} ., 1990; White \emph{et al.,} 2008). The annual
fluctuations are characterized by variable water quality changes ranging
from poor to good water quality years with resultant impacts on lake
ecology (Hickley \emph{et al} ., 2004) and livelihoods (Aura \emph{et
al} ., 2020). In the long-term, the lake level has fluctuated from a low
of 1.47 m in 1956 to peak levels (13.95, m) in 2017.

The results showed relations between WLF indices and lake water quality
variables and with lake fisheries yields over time. These relations
suggest a significant influence of water level fluctuations in the
ecological functions of the lake. The results also indicated the impact
of hydrological variable changes on the lake's fisheries productivity
through some mechanisms related to water-level mediated changes in fish
species catchability and condition factor as also reported for Lake
Turkana in Kenya (Kolding \emph{et al} ., 1993a) and elsewhere
(Kolding\emph{et al} ., 2012). Studies on concordance between lake WLFs
and biota conducted elsewhere (Neckles, \emph{et al.,} 1990; White
\emph{et al.,}2008; Gownaris \emph{et al} ., 2015) showed similar
patterns between WLFs and biota variables demonstrating the important
role of water levels in a lakes' habitat availability,
macroinvertebrates' richness and their temporal distribution.

The concordance of water quality variables with WLFs is expected as
water levels are directly controlled by hydrological inputs driven by
severe droughts and floods in the region. A rise or draw-down estimated
at 1 m in water level causes a shift in the lake's hydrological budget
that might affect the ecological process in the lake. The relationship
between water level (WL) and the lake surface area indicated that a 1 m
increase in water level leads to about 90 km\textsuperscript{2} change
in Lake Baringo's surface area. This finding is a demonstration that
high water levels create flood pulse regimes that provide enhanced
habitat, food, and breeding areas for fish species that contribute to
increased fisheries yields (Gownaris \emph{et al} ., 2015). The littoral
habitat area is important for enhanced fisheries production in semi-arid
lakes such as Baringo. For instance, in Lake Baringo, the lake level
decline leads to large losses of the open water habitat thereby likely
reducing the carrying capacity of species that dominate its pelagic
zone. Moreover, the lake level decline shrinks the floodplain area and
leads to losses of littoral habitats, feeding and breeding habitats of
some species (Karenge and Kolding, 1995; Grown \emph{et al} ., 2015;
Mageria and Kibwage, 2009). This might probably be the reason for the
low catchability of some commercially valuable fishery species
(e.g.\emph{O. niloticus barinoensis} and \emph{B. intermediens} ) of
Lake Baringo during the years of lower levels in the lake (Mlewa
\emph{et al} ., 2005; Nyakeya \emph{et al} ., 2020). In Lake Turkana,
Kenya, the majority of the lake's endemic species are found below the
10-m contour indicating that declines in inshore and offshore habitats
would have severe ecological consequences for these species (Hopson,
1982).

The effect of WLFs on habitat variations and their effects on the
fisheries of Lake Baringo is also a function of the depth of the lake
and human population density settlements around the lake. The three
zones of study (north, central, and south) in the lake will be
differently affected depending on their depths and the human activities
along the shores (Welcomme, 2008). Therefore as a result of changes in
lake level, there is increased interaction between the aquatic and
terrestrial ecotones during the rising water level years requiring an
integrated approach to the management of the lake-terrestrial
ecosystems. Regression and Gaussian analyses outputs demonstrated the
highly responsive nature of turbidity and WQI to increasing DLTM
indicating that hydrological indices are the major drivers of the water
quality changes in the lake. We found positive correlations between
nutrient components and WLFs except nitrogen forms
(NO\textsubscript{2}\textsuperscript{--},
NO\textsubscript{3}\textsuperscript{--},
NH\textsubscript{4}\textsuperscript{+}) and Chl-a which either showed
negative relation with WLFS or did not demonstrate any relationship (TN
and Chl-a) with any WLFs indices due probably to the decrease of these
nutrient concentrations and increase of Chl-a levels with the increased
DLTM in the lake. These results are in agreement with the findings from
most tropical reservoirs and lakes. For example, in Lake Tana, Ethiopia,
the plant nutrients are quickly exhausted during draw-downs and their
effects on biological production become less important (Karengea and
Kolding, 1995). The results of this study also indicated that the years
with decreased WLs were characterized by lower lake primary production
measured as chlorophyll-a concentrations indicating the effect of WLFs
on the lake production in terms of phytoplankton biomass and likely the
reported zooplankton limitation in Lake Baringo (Schagerl and Oduor,
2003; Tarras-Wahlberg \emph{et al} ., 2003).

CONCLUSION

The results of the study highlight the link between water quality
properties and WLFs in Lake Baringo. The lake's fishery yields and
species condition factors appear to be also influenced by WL changes.
These linkages result from the influence of WLFs indices on the critical
water quality parameters like turbidity, DO, conductivity, temperature,
and the water quality index WQI). There is a direct link between years
of high lake level and increased fisheries landings perhaps mediated
through increased habitat availability as nursery and feeding grounds
for species.

During periods of droughts with less inflow, water level decrease leads
to a decline in littoral habitat due to the water volume reduction and
this might force the species including juveniles to find a refugee in
open water habitat enhancing the predatory degree and reducing the
refugia areas in the lake for juveniles and some species
(eg.\emph{Oreochromis niloticus baringoensis} ). If the water level
declines severely, the lake's physio-chemical characteristics will be
altered with an eventual reduction in DO due to the decrease of the
euphotic zone in the lake, increase in water temperature, and high
conductivity making the conditions in the lake harmful for the aquatic
communities. This emphasizes the need for long-term monitoring of the
lake's condition and catchment for purposes of integrated lake
management. Such management will require monitoring of upstream
developments in order to maintain natural inflows during draw-down
seasons.

ACKNOWLEDGMENTS

This research was funded by European Union (EU) through the
Collaborative Training in Fisheries and Aquaculture in Eastern, Central,
and Southern Africa Project (COTRA project). We thank the Kenya
Fisheries Department and Water Resource Authority (WARA) for providing
long-term data on fishery and water levels of the lake. Our thanks go to
the technicians from KMFRI at Kisumu station, Mrs. Mwanchi, for nutrient
analysis and Benjamin Bett Arwait, Julius Kiplagat, Barongo, Caroline
Kibet, and Winnie Chelagat from KMFRI at Baringo station for assistance
in the field and sample analysis.

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DATA AVAIL ABILITY STATEMENT

The data that support the findings of this study are available from the
corresponding author upon reasonable request.

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